update versions of accelerate and diffusers

README.md

@@ -22,7 +22,14 @@ __Stable Diffusion web UI now seems to support LoRA trained by ``sd-scripts``.__
 
 The feature of SDXL training is now available in sdxl branch as an experimental feature. 
 
-Sep 3, 2023: The feature will be merged into the main branch soon. Following are the changes from the previous version. 
+Sep 24, 2023: The feature will be merged into the main branch very soon. Following are the changes from the previous version. 
+
+- `accelerate` is updated to 0.23.0, and `diffusers` is updated to 0.21.2. Please update them with the upgrade instructions below.
+- Intel ARC support with IPEX is added. [#825](https://github.com/kohya-ss/sd-scripts/pull/825)
+- Other changes and fixes.
+- Thanks for contributions from Disty0, sdbds, jvkap, rockerBOO, Symbiomatrix and others!
+
+Sep 3, 2023: 
 
 - ControlNet-LLLite is added. See [documentation](./docs/train_lllite_README.md) for details.
 - JPEG XL is supported. [#786](https://github.com/kohya-ss/sd-scripts/pull/786) 

networks/lora_diffusers.py

@@ -117,7 +117,7 @@ class LoRAModule(torch.nn.Module):
         super().__init__()
         self.lora_name = lora_name
 
-        if org_module.__class__.__name__ == "Conv2d":
+        if org_module.__class__.__name__ == "Conv2d" or org_module.__class__.__name__ == "LoRACompatibleConv":
             in_dim = org_module.in_channels
             out_dim = org_module.out_channels
         else:
@@ -126,7 +126,7 @@ class LoRAModule(torch.nn.Module):
 
         self.lora_dim = lora_dim
 
-        if org_module.__class__.__name__ == "Conv2d":
+        if org_module.__class__.__name__ == "Conv2d" or org_module.__class__.__name__ == "LoRACompatibleConv":
             kernel_size = org_module.kernel_size
             stride = org_module.stride
             padding = org_module.padding
@@ -166,7 +166,8 @@ class LoRAModule(torch.nn.Module):
             self.org_module[0].forward = self.org_forward
 
     # forward with lora
-    def forward(self, x):
+    # scale is used LoRACompatibleConv, but we ignore it because we have multiplier
+    def forward(self, x, scale=1.0):
         if not self.enabled:
             return self.org_forward(x)
         return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
@@ -318,8 +319,12 @@ class LoRANetwork(torch.nn.Module):
             for name, module in root_module.named_modules():
                 if module.__class__.__name__ in target_replace_modules:
                     for child_name, child_module in module.named_modules():
-                        is_linear = child_module.__class__.__name__ == "Linear"
-                        is_conv2d = child_module.__class__.__name__ == "Conv2d"
+                        is_linear = (
+                            child_module.__class__.__name__ == "Linear" or child_module.__class__.__name__ == "LoRACompatibleLinear"
+                        )
+                        is_conv2d = (
+                            child_module.__class__.__name__ == "Conv2d" or child_module.__class__.__name__ == "LoRACompatibleConv"
+                        )
 
                         if is_linear or is_conv2d:
                             lora_name = prefix + "." + name + "." + child_name
@@ -359,7 +364,7 @@ class LoRANetwork(torch.nn.Module):
             skipped_te += skipped
         print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.")
         if len(skipped_te) > 0:
-            print(f"skipped {len(skipped_te)} modules because of missing weight.")
+            print(f"skipped {len(skipped_te)} modules because of missing weight for text encoder.")
 
         # extend U-Net target modules to include Conv2d 3x3
         target_modules = LoRANetwork.UNET_TARGET_REPLACE_MODULE + LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
@@ -368,7 +373,7 @@ class LoRANetwork(torch.nn.Module):
         self.unet_loras, skipped_un = create_modules(True, None, unet, target_modules)
         print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
         if len(skipped_un) > 0:
-            print(f"skipped {len(skipped_un)} modules because of missing weight.")
+            print(f"skipped {len(skipped_un)} modules because of missing weight for U-Net.")
 
         # assertion
         names = set()

requirements.txt

@@ -1,6 +1,6 @@
-accelerate==0.19.0
+accelerate==0.23.0
 transformers==4.30.2
-diffusers[torch]==0.18.2
+diffusers[torch]==0.21.2
 ftfy==6.1.1
 # albumentations==1.3.0
 opencv-python==4.7.0.68
@@ -15,8 +15,6 @@ easygui==0.98.3
 toml==0.10.2
 voluptuous==0.13.1
 huggingface-hub==0.15.1
-# for loading Diffusers' SDXL
-invisible-watermark==0.2.0 
 # for BLIP captioning
 # requests==2.28.2
 # timm==0.6.12